Histidine
Background
Section titled “Background”Histidine is an alpha-amino acid that is fundamental to human biology. It is categorized as an essential amino acid, meaning the human body cannot synthesize it and must acquire it through dietary intake. The distinguishing feature of histidine is its imidazole side chain, which allows it to function as a proton donor or acceptor near physiological pH.
Biological Basis
Section titled “Biological Basis”Within the body, histidine is integral to a wide array of biological processes. It serves as a primary building block for the synthesis of proteins, contributing to both structural components and enzymatic functions. Beyond its role in protein formation, histidine is a precursor for several critical biomolecules. Notably, it is decarboxylated to produce histamine, a compound essential for immune responses, allergic reactions, and neurotransmission. Histidine is also a precursor to carnosine, a dipeptide concentrated in muscle and brain tissues, recognized for its antioxidant and pH-buffering capabilities. The imidazole ring of histidine is frequently found in the active sites of enzymes, where its ability to exchange protons facilitates catalytic activity. Furthermore, its buffering capacity helps maintain physiological pH balance, particularly within hemoglobin, where it aids in the transport of oxygen and carbon dioxide.
Clinical Relevance
Section titled “Clinical Relevance”Alterations in histidine metabolism or insufficient dietary intake can have implications for health. While a deficiency is uncommon in individuals consuming a balanced diet, it can lead to compromised protein synthesis and other metabolic imbalances. Genetic conditions affecting histidine breakdown, such as histidinemia, an inherited disorder resulting from a deficiency of the enzyme histidase, can cause elevated histidine levels in the blood and urine. Ongoing research investigates the involvement of histidine and its derivatives in various health conditions, including inflammatory responses, neurological function, and cardiovascular health, owing to its roles in histamine production and antioxidant defense.
Social Importance
Section titled “Social Importance”The essential nature of histidine underscores the significance of a nutritious diet for maintaining adequate intake. It is abundant in diverse protein-rich foods, including meats, fish, dairy products, and certain plant-based options. For individuals with specific dietary requirements or restrictions, understanding the sources of essential amino acids like histidine is vital for effective nutritional planning. The continued study of histidine, its metabolic pathways, and genetic factors influencing its levels contributes significantly to a comprehensive understanding of human health, nutrition, and strategies for disease prevention.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genome-wide association studies (GWAS) are inherently susceptible to various methodological and statistical limitations that can impact the interpretation and generalizability of findings. Many studies suffer from moderate sample sizes, which can lead to inadequate statistical power and an increased likelihood of false negative findings, potentially missing true genetic associations with a trait.[1] Furthermore, the reliance on imputation based on specific HapMap builds, such as HapMap build35 or release 22 CEU phased genotypes, means that the comprehensiveness and accuracy of imputed data are dependent on the reference panel used, potentially limiting the discovery of all causal variants and affecting the quality of meta-analyses.[2]Fixed-effects meta-analysis, while useful, assumes a common effect size across studies and may not fully capture between-study heterogeneity, potentially leading to biased combined estimates if significant variation exists.[2] Replication challenges are also common, with a significant proportion of initially reported phenotype-genotype associations failing to replicate in subsequent studies. This can stem from several factors, including initial false positive findings, differences in study cohorts, or insufficient statistical power in replication attempts.[1] Moreover, the practice of performing sex-pooled rather than sex-specific analyses may obscure genetic variants that have associations only in one sex, leading to undetected, sex-specific genetic effects.[3] While methods exist to account for relatedness in family or founder populations, such as variance component models, improper handling can lead to inflated false-positive rates and misleading P-values, thus compromising the validity of association signals.[4]
Population Homogeneity and Generalizability
Section titled “Population Homogeneity and Generalizability”A significant limitation of many GWAS is the lack of diversity in study populations, which predominantly consist of individuals of white European ancestry, often from middle-aged to elderly cohorts.[1] This demographic homogeneity inherently restricts the generalizability of findings to younger individuals or those of other ethnic or racial backgrounds, as genetic architecture and environmental exposures can vary significantly across diverse populations. The exclusive focus on specific ancestries, such as Caucasian or founder populations like the SardiNIA sample, means that genetic variants identified may not be relevant or have the same effect sizes in other groups, hindering the translation of research into broader public health applications.[1] Additionally, specific cohort characteristics can introduce biases; for example, studies where DNA was collected at later examinations may be subject to survival bias, where only individuals who lived long enough to participate are included, potentially skewing observed genetic associations.[1] Such biases limit the ability to infer genetic effects across the entire lifespan or in populations with different health profiles. The findings derived from these specific cohorts, while valuable, must therefore be interpreted cautiously regarding their universal applicability, underscoring the need for more ethnically diverse and age-representative studies to enhance generalizability.
Phenotypic Complexity and Unexplained Variation
Section titled “Phenotypic Complexity and Unexplained Variation”Understanding the genetic basis of complex traits is further complicated by challenges related to phenotypic measurement and the persistent issue of unexplained variation. Many quantitative traits, such as protein levels, do not follow a normal distribution, necessitating statistical transformations (e.g., log, Box-Cox, or probit transformations) to meet the assumptions of association tests.[5] While these transformations address statistical requirements, they can complicate the direct interpretation of effect sizes and their biological relevance. Furthermore, phenotypes are sometimes derived from the mean of multiple observations or from specific populations like monozygotic twins, which can influence the estimated effect sizes and the proportion of variance explained in the broader population if not carefully adjusted.[6]Current GWAS arrays, even those considered comprehensive, utilize only a subset of all existing single nucleotide polymorphisms (SNPs) in the human genome, meaning that some genes or causal variants may be missed due to incomplete coverage or lack of strong linkage disequilibrium with genotyped SNPs.[3] This limitation contributes to the “missing heritability” problem, where identified genetic variants explain only a fraction of the total phenotypic variance, as seen in traits where only approximately 40% of genetic variation is explained.[6] The remaining unexplained variation is likely influenced by complex environmental factors, gene-environment interactions, rare variants, and epigenetic modifications, which are often not fully captured or accounted for in current GWAS designs.[1] Therefore, while GWAS has been successful in identifying novel genetic loci, a complete understanding of the genetic architecture of complex traits requires integrating these findings with broader environmental and biological contexts.
Variants
Section titled “Variants”Genetic variations play a crucial role in shaping an individual’s biochemical profile, including the metabolism and regulation of essential amino acids like histidine. Several genes and their specific single nucleotide polymorphisms (SNPs) are implicated in pathways that directly or indirectly influence histidine levels and related physiological processes. These variants can affect enzyme activity, transporter function, or broader cellular signaling, thereby impacting a range of biological traits.
Variants within genes central to amino acid metabolism and transport can profoundly affect histidine homeostasis. For instance, theHALgene, encoding Histidine ammonia-lyase, is vital for the first step in histidine catabolism, converting histidine into urocanic acid. Variations such asrs61937878 , rs117991621 , and rs143854097 in HALcould alter the efficiency of this enzyme, leading to changes in circulating histidine levels. Similarly, theCPS1gene, Carbamoyl Phosphate Synthetase 1, is a key enzyme in the urea cycle, essential for detoxifying ammonia and integrating nitrogen metabolism, a pathway that can be influenced by amino acid breakdown products. Variants likers1047891 and rs715 in CPS1 may impact its function. The SLC6A19 gene (Solute Carrier Family 6 Member 19), part of the TERLR1 - SLC6A19locus, is a major transporter for neutral amino acids, including histidine, in the kidney and intestine, and its variants, such asrs11133665 , could affect the reabsorption and availability of histidine. Genetic variation, including single nucleotide polymorphisms, is a fundamental aspect of human biology that can be studied through genome-wide association analyses.[7]Other genetic variants influence inflammatory and coagulation pathways, which can indirectly interact with histidine-related processes. TheKNG1 gene (Kininogen 1), found within the KNG1, HRG-AS1 locus, encodes kininogen, a precursor to kinins, which are potent mediators of inflammation and blood pressure regulation. Variants such as rs5030062 in this region have been associated with dyslipidemia.[8] Plasma kallikrein, encoded by KLKB1, cleaves kininogen to release these kinins, and variants like rs3733402 and rs1912826 in KLKB1may modulate this system. Histidine residues within proteins like kininogen are crucial for their structure and function. TheF12 gene, encoding Coagulation Factor XII, is a component of the intrinsic coagulation pathway, and its variants, such as rs2731673 and rs2545801 (also affecting GRK6), could influence blood clot formation. Other coagulation factors have been identified through genetic studies.[9] Furthermore, the MIP gene family (Macrophage Inflammatory Proteins), which includes chemokines like CCL3 and CCL4, plays a central role in immune response and inflammation. Variants such as rs2939302 and rs2933243 could impact the signaling of these inflammatory mediators, and a cluster of cytokine genes includingCCL3 and CCL4 has been associated with kidney function .
Beyond direct metabolic roles, variants in genes involved in general cellular regulation and stress responses can also have broad physiological consequences. GRK6 (G Protein-Coupled Receptor Kinase 6) is involved in regulating G protein-coupled receptor signaling, a fundamental process in cellular communication. Variants like rs2731673 and rs2545801 (shared with F12) could affect signal transduction pathways that may indirectly interact with histidine-dependent processes. TheCYP4V2 gene, part of the CYP4V2 - KLKB1 locus, encodes a cytochrome P450 enzyme involved in fatty acid metabolism, and its variant rs11132382 could influence lipid profiles and cellular energy balance. The CCDC38 gene (Coiled-Coil Domain Containing 38) and its variants, including rs4762641 , rs75341400 , and rs7954638 , are less characterized but often implicated in structural or regulatory roles within the cell. Similarly, NDRG2 (N-Myc Downstream Regulated Gene 2) is a tumor suppressor gene involved in cell differentiation, growth, and stress responses, with variants such as rs1998848 , rs35291299 , and rs1958393 potentially affecting these crucial cellular processes. Genome-wide association studies have been instrumental in identifying genetic variations associated with complex traits.[10]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs61937878 rs117991621 rs143854097 | HAL | vitamin D amount gamma-glutamylhistidine measurement histidine measurement imidazole lactate measurement N-acetylhistidine measurement |
| rs11132382 | CYP4V2 - KLKB1 | persulfide dioxygenase ETHE1, mitochondrial measurement blood protein amount leucine measurement histidine measurement |
| rs2731673 rs2545801 | GRK6, F12 | vascular endothelial growth factor D measurement dipeptidase 2 measurement tRNA (guanine-N(7)-)-methyltransferase measurement transmembrane protein 87B measurement neurexin-1 measurement |
| rs3733402 rs1912826 | KLKB1 | IGF-1 measurement serum metabolite level BNP measurement venous thromboembolism vascular endothelial growth factor D measurement |
| rs1047891 rs715 | CPS1 | platelet count erythrocyte volume homocysteine measurement chronic kidney disease, serum creatinine amount circulating fibrinogen levels |
| rs4762641 rs75341400 rs7954638 | CCDC38 | histidine measurement |
| rs11133665 | TERLR1 - SLC6A19 | urinary metabolite measurement kynurenine measurement N-acetyl-1-methylhistidine measurement methionine sulfone measurement Methionine sulfoxide measurement |
| rs5030062 | KNG1, HRG-AS1 | plasma renin activity measurement CD84/ITGA6 protein level ratio in blood BCL2L11/ITGA6 protein level ratio in blood BCL2L11/RAB6A protein level ratio in blood blood protein amount |
| rs2939302 rs2933243 | MIP | glutamine measurement gamma-glutamylglutamine measurement bilirubin measurement serum urea amount serum metabolite level |
| rs1998848 rs35291299 rs1958393 | NDRG2 | total cholesterol measurement low density lipoprotein cholesterol measurement histidine measurement glutamine measurement |
References
Section titled “References”[1] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S11.
[2] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 5, 2008, pp. 520–528.
[3] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S10.
[4] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161–169.
[5] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, p. e1000072.
[6] Benyamin, B., et al. “Variants in TF and HFEexplain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 84, no. 1, 2009, pp. 60–65.
[7] Kooner, J. S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149-51.
[8] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.
[9] Reiner, A. P., et al. “Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein.”Am J Hum Genet, vol. 82, no. 5, 2008, pp. 1193-201.
[10] Heid, I. M., et al. “Genome-wide association analysis of high-density lipoprotein cholesterol in the population-based KORA study sheds new light on intergenic regions.”Circ Cardiovasc Genet, vol. 3, no. 1, 2010, pp. 24-34.